Information processing device, information generating method, and computer program

Abstract

A device according to one embodiment of the present disclosure is an information processing device that generates an event related to road traffic. This information processing device is provided with: a host device or an external device storage that stores a detection object range in which a vehicle can be present and a plurality of time point SAR images including the detection object range; and a control unit that detects a state change of the SAR image in the detection object range and generates the event on the basis of the detected state change.

Description

Information processing device, information generation method, and computer program

[0001] This disclosure relates to an information processing device, an information generation method, and a computer program. This application claims priority to Japanese Application No. 2023-150152 filed on September 15, 2023, and incorporates by reference all of the contents of that Japanese application.

[0002] Patent Document 1 describes a vehicle detection method using a vehicle detection device having an object sensor that detects the presence or absence of a vehicle and a speed sensor that detects the speed of the vehicle. The vehicle detection method of Patent Document 1 includes a step of determining, when a predetermined vehicle detection device detects the presence of a vehicle but does not detect its speed, that the vehicle is parked or stopped on the road or that the vehicle is completely stopped and congested, based on the detection result of another vehicle detection device nearby on the same road.

[0003] Patent Document 2 describes a position estimation device that displays a target position detected from the difference between synthetic aperture radar images (SAR images) on a road map. The position estimation device in Patent Document 2 includes a road detection unit that detects roads within a predetermined range centered on the corrected target position from a road map, a distance comparison unit that detects, from the detected roads, roads that are closest to the corrected target position in the direction of travel of the platform, and a display unit that displays the detected position on the road as the true target position on the road map.

[0004] Patent Literature 3 describes a determination device that determines an appropriate investigation area. The determination device of Patent Literature 3 includes an extraction means that extracts a candidate area for disaster situation investigation using sensor information acquired by a sensor information acquisition device, an acquisition means that acquires ground surface changes that are the result of an analysis using measurement results from a ground surface measurement device in the candidate area, and a determination means that determines an investigation area for the disaster situation in the candidate area using the ground surface changes.

[0005] Japanese Patent No. 4321143 Japanese Patent Application Laid-Open No. 2016-173239 WO2023 / 017612

[0006] An apparatus according to one aspect of the present disclosure is an information processing apparatus that generates events related to road traffic, and includes: a storage device in the apparatus itself or an external apparatus that stores a detection target range in which a vehicle may exist and SAR images at multiple points in time that include the detection target range; and a control unit that detects changes in the state of the SAR images within the detection target range and generates the event based on the detected changes in state.

[0007] The embodiments of the present disclosure may be realized by an apparatus, a system, a method, an integrated circuit, a computer program, or a non-transitory computer-readable recording medium, or any combination thereof. The recording medium may be either volatile or non-volatile. The apparatus may be composed of multiple individual devices. When composed of multiple individual devices, they may be arranged in a single housing or may be arranged separately in two or more separate housings.

[0008] Fig. 1 is a schematic diagram showing an example of the configuration of an information providing system. Fig. 2 is a block diagram showing an example of the configuration of an information providing server and an in-vehicle device. Fig. 3 is a flowchart showing an example of the process of generating and distributing information to be provided. Fig. 4 is a flowchart showing another example of the process of generating and distributing information to be provided. Fig. 5 is an explanatory diagram showing an example of a road map screen display including events provided by the information providing server.

[0009] <Problem to be Solved by the Present Disclosure> The above-mentioned patent documents do not anticipate an appropriate method for generating road traffic-related events, which are difficult to generate using probe information, using SAR images as source data. In view of the above-mentioned conventional problems, the present disclosure aims to provide an information processing device and the like that can appropriately generate road traffic-related events.

[0010] Effect of the Present Disclosure According to the present disclosure, events related to road traffic can be appropriately generated.

[0011] <Outline of Embodiments of the Present Disclosure> Below, an outline of embodiments of the present disclosure will be listed and described.

[0012] (1) The device according to this embodiment is an information processing device that generates events related to road traffic, and includes a storage device in the device itself or an external device that stores a detection target range in which a vehicle may exist and SAR images at multiple points in time that include the detection target range, and a control unit that detects changes in the state of the SAR images within the detection target range and generates the event based on the detected changes in state.

[0013] According to the information processing device of this embodiment, the control unit detects changes in the state of SAR images within the detection range and generates events based on the detected changes in state, making it possible to generate events related to road traffic that are difficult to generate using probe information alone. Therefore, it is possible to appropriately generate events related to road traffic.

[0014] (2) In the information processing device of (1) described above, the state change detected by the control unit may be a state change of the SAR image at a predetermined generation time point relative to the SAR image at a time point prior to the predetermined generation time point. In this case, there is no need to generate a background SAR image, which will be described later, and therefore the processing load required for detecting the state change can be reduced.

[0015] (3) In the information processing device of (1) described above, the storage may further store a background SAR image obtained by excluding moving objects from the plurality of SAR images, and the state change detected by the control unit may be a state change of the SAR image at the time of generation relative to the background SAR image generated before a predetermined generation time. In this case, since the state change is determined using the background SAR image, there is an advantage that false detections can be reduced compared to when a SAR image generated before the predetermined generation time is used as is, and detection accuracy can be improved.

[0016] (4) In the information processing device according to any one of (1) to (3), the control unit may further generate prediction information based on statistical information of the detected state changes. In this case, by distributing the generated prediction information to a user, not only events based on state changes that have actually occurred in the SAR image but also the generated prediction information can be provided to the user as events that may occur in the future.

[0017] (5) In the information processing device according to (1) to (4), the events may include at least one of roadside parking, the presence of an obstacle, the possibility of a road collapse, a traffic jam at an exit of a highway, a full parking lot, and an empty parking lot. In this case, it is possible to generate each of the above events that are difficult to generate using probe information.

[0018] (6) In the information processing device according to any one of (1) to (5), the events may include at least one of a signboard, a road impassable section, a stranded vehicle, a recommended parking lot, a congestion at an entrance / exit to a facility, a puddle on a road, and a flooded section of a road. In this case, it is possible to generate each of the above events that are difficult to generate using probe information.

[0019] (7) The method according to this embodiment is an information generation method performed by the information processing devices described above in (1) to (6). Therefore, the information generation method according to this embodiment has the same effects as the information processing devices described above in (1) to (6).

[0020] (8) The computer program according to this embodiment is a computer program for causing a computer to function as the information generation method described above in (1) to (6). Therefore, the computer program according to this embodiment has the same effects as the information processing device described above in (1) to (6).

[0021] <Details of the Embodiments of the Present Invention> Hereinafter, the details of the embodiments of the present invention will be described with reference to the drawings. Note that at least some of the embodiments described below may be combined in any manner.

[0022] [Definition of Terms] Before describing the details of this embodiment, the terms used in this specification will first be defined. "Vehicle": refers to all vehicles traveling on roads. The drive system of the vehicle is not limited to internal combustion engines, and electric vehicles and hybrid cars are also included in the vehicle. In this embodiment, when simply referring to a "vehicle," it includes both a probe vehicle having an on-board device capable of transmitting probe information and a normal vehicle that does not have such an on-board device.

[0023] "Probe information" refers to information including various data about a vehicle sensed by the vehicle while it is traveling on a road. Probe information is also called probe data or floating car data. Probe information may include various vehicle attribute data such as vehicle identification information, vehicle position, vehicle speed, vehicle direction, and the time of occurrence of these data.

[0024] "Probe vehicle": A vehicle capable of transmitting probe information to an external device such as a server. More specifically, a vehicle that senses vehicle attribute data and transmits probe information including the sensed data to an external device.

[0025] "Synthetic aperture radar image": Data observed by a synthetic aperture radar (SAR), in which pixel values ​​represent gray levels corresponding to the backscattering intensity at each point on the Earth's surface. Also called a "SAR image." Examples of data formats that can be used for SAR images include the "Committee on Earth Observation Satellites (CEOS) format" and "GeoTIFF," a tagged image file format (TIFF) with location information.

[0026] The SAR image may be any of a "single-polarized SAR image" that visualizes the backscattering intensity of a single polarized wave at a single point in time, a "dual-polarized SAR image" that visualizes the difference in scattering characteristics of each polarized wave at a single point in time, and an "intensity difference SAR image" that visualizes the change in the backscattering intensity of a single polarized wave. The product processing level of the SAR image may be any of "Level 1.1," "Level 1.5," "Level 2.1," and "Level 3.1," but from the viewpoint of saving the user the trouble of correction processing, etc., higher level data is preferable.

[0027] "Optical image": Data in which the level of visible light at each point on the Earth's surface is used as a pixel value, as observed by an optical satellite or an aircraft. Optical image data formats include "BMP (Bit Map)," "JPEG (Joint Photographic Experts Group)," "RAW format," and "GeoTIFF." Image processing in the information providing server 2 is performed after unifying the data formats of optical images and SAR images.

[0028] [System Configuration Example] Fig. 1 is a schematic diagram showing a configuration example of an information providing system 1. Fig. 2 is a block diagram showing a configuration example of an information providing server 2 and an in-vehicle device 4 included in the information providing system 1. As shown in Figs. 1 and 2, the information providing system 1 includes an information providing server 2, an in-vehicle device 4 of a probe vehicle 3, a user terminal 6 carried by a user 5, an SAR image server 10, and an optical image server 11. Hereinafter, the information providing server 2 may be abbreviated as "server 2."

[0029] The information providing system 1 of this embodiment is a system in which an information providing server 2 generates predetermined information to be provided about road traffic (for example, events E1 to E7 in FIG. 5 ) and distributes the generated information to an in-vehicle device 4 of a vehicle 3, a user terminal 6, etc. Therefore, the information providing server 2 is a type of information processing device that generates information to be provided (events) about road traffic.

[0030] The provided information (events) generated by the server 2 includes information on road congestion, information on roadside parking (hereinafter also referred to as "roadside parking"), information on the status of parking lots, etc. This provided information is generated using probe information, SAR images, etc. as source data.

[0031] The information providing server 2 is operated by an automobile manufacturer or an IT (Information Technology) company that operates various information distribution businesses. The information providing server 2 may be either an on-premise server or a cloud server. In response to a search request from the in-vehicle device 4 or the user terminal 6, the information providing server 2 can also execute a route search process based on a predetermined algorithm such as the Dijkstra algorithm or the potential method. In this case, the server 2 transmits the searched driving route to the sender of the search request.

[0032] The SAR image server 10 is operated by a private company or government agency that distributes SAR images of a specific area. The SAR images are generated by performing predetermined image processing on data received from a SAR satellite. The SAR image server 10 may be either an on-premise server or a cloud server.

[0033] The optical image server 11 is operated by a private company or government agency that distributes optical images of a specific area. Optical images are generated by performing predetermined image processing on data received from an optical satellite or aircraft. The optical image server 11 may be either an on-premise server or a cloud server. If a single organization distributes both SAR and optical images, the SAR image server 10 and the optical image server 11 may be the same server.

[0034] The in-vehicle device 4 of the vehicle 3 is capable of wireless communication with wireless base stations 7 (e.g., mobile base stations) in various locations. The wireless base stations 7 are capable of communicating with the information providing server 2 via a public communication network 8 including the Internet. Therefore, the in-vehicle device 4 can wirelessly transmit uplink information S1 addressed to the server 2 to the wireless base stations 7. In addition, the server 2 can transmit downlink information S2 addressed to a specific in-vehicle device 4 to the public communication network 8.

[0035] The user terminal 6 is a data communication terminal carried by the user 5, such as a smartphone, tablet computer, or laptop computer. The user terminal 6 is capable of wireless communication with wireless base stations 7 in various locations. Therefore, the user terminal 6 can wirelessly transmit uplink information S1 addressed to the server 2 to the wireless base station 7. The information providing server 2 can transmit downlink information S2 addressed to a specific user terminal 6 to the public communication network 8.

[0036] 1 and 2 show the user terminal 6 as a mobile terminal, but the user terminal 6 may also be a computer such as a desktop personal computer installed indoors. In this case, the user terminal 6 may communicate with the information providing server 2 via a fixed communication network that is connected to the public communication network 8 via an optical communication line or the like. The information providing server 2 may also provide information to another server computer (not shown) that communicates via the public communication network 8 or the like.

[0037] [Configuration of Information Providing Server] As shown in FIG. 2, the information providing server 2 is a server computer including a control unit 21, a storage unit 22, a communication unit 23, and a plurality of databases 24, 25, 26, 27, and 28 constructed in the storage unit 22 and the like.

[0038] The control unit 21 is an arithmetic processing device including a CPU (Central Processing Unit) and a RAM (Random Access Memory). The control unit 21 may include an integrated circuit other than the CPU, such as an FPGA (Field-Programmable Gate Array). The control unit 21 reads a computer program 29 stored in the storage unit 22 into the memory (RAM) and performs predetermined information processing in accordance with the read program 29.

[0039] The storage unit 22 is an auxiliary storage device including a non-volatile memory such as a hard disk drive (HDD) and a solid state drive (SSD). The storage unit 22 may also include a flash read-only memory (ROM), a universal serial bus (USB) memory, or an SD card. The computer programs 29 stored in the storage unit 22 include a program for calculating link travel times and congestion lengths, a route search program, and a program for causing the CPU to execute object detection processing based on SAR images (FIGS. 3 and 4).

[0040] The communication unit 23 is a communication interface that communicates with the wireless base station 7 via the public communication network 8. The communication unit 23 can receive uplink information S1 that the wireless base station 7 has transmitted to the device itself, and can transmit downlink information S2 that the device itself has generated to the wireless base station 7. The communication unit 23 is also connected to the SAR image server 10 and the optical image server 11 via the public communication network 8. The communication unit 23 periodically receives image data from each of the servers 10, 11 at a predetermined frequency. The communication unit 23 transfers the received image data to the control unit 21.

[0041] The multiple databases 24, 25, 26, 27, and 28 are constructed in large-capacity storage such as an HDD or SSD included in the storage unit 22. The large-capacity storage of the storage unit 22 may be one or more external storage devices connected to the information providing server 2. At least one of the multiple databases 24, 25, 26, 27, and 28 may be constructed in the storage of a cloud server operated by an IT company other than the information providing server 2.

[0042] In this way, the memory unit 22 in the sense of storage in which the multiple databases 24, 25, 26, 27, and 28 are constructed may be a memory unit of the information providing server 2's own device or an external device (such as the above-mentioned external device or cloud server). The multiple databases 24, 25, 26, 27, and 28 include the map database 24, the optical image database 25, the target range database 26, the SAR image database 27, and the detected object database 28.

[0043] The map database 24 stores, for example, digital road map data (hereinafter referred to as "DRM") covering the entire country. The DRM includes "intersection data" and "link data." The "intersection data" is data that associates intersection IDs assigned to domestic intersections with location information of the intersections. The "link data" is data that associates the following information a to e with link IDs of specific links assigned to domestic roads.

[0044] Information a: Position information of the start point, end point, and interpolation point of a specific link Information b: Link ID connected to the start point of a specific link Information c: Link ID connected to the end point of a specific link Information d: Link cost of a specific link

[0045] The DRM forms a network corresponding to the actual road alignment and driving direction of the road. In other words, the DRM is a network in which road sections between nodes representing intersections are connected by directed links l (lowercase L). More specifically, the DRM is a directed graph in which a node n is set for each intersection and each node n is connected by a pair of directed links l in opposite directions. Therefore, in the case of a one-way road, only one-way directed links l connect the node n.

[0046] The DRM also includes road attribute information of the road corresponding to the directed link l. The road attribute information includes, for example, the following information 1 to information 6: Information 1: Road type information indicating whether the road is an ordinary road or a toll road Information 2: Average gradient of the road Information 3: Number of lanes on the road Information 4: Road width for each lane Information 5: Radius of curvature of the road Information 6: Regulated speed limit of the road (for example, legal speed limit)

[0047] The optical image database 25 stores optical images of a predetermined area received at a predetermined frequency from the optical image server 11. When an optical image of the same area is acquired, it is successively updated with the latest optical image.

[0048] The detection target range TA is stored in the target range database 26. The detection target range TA is a geographical area in which the process of detecting state changes based on SAR images is executed. Specifically, the detection target range TA is made up of polygon data PD1 and PD2 that represent the outer limits of areas within which the presence of a vehicle 3 can be determined from the sky, such as roads and parking lots. For example, in the example of FIG. 2 , PD1 represents polygon data for roads, and PD2 represents polygon data for parking lots.

[0049] The SAR image database 27 stores SAR images SIi (i = 1, 2 ...) received at a predetermined frequency from the SAR image server 11. The subscript i of the symbol SI representing the SAR image is an identification value that indicates the oldest SAR image created by the SAR image server 11, with larger values ​​indicating newer images. Furthermore, if the server 2 acquires multiple SAR images SI from the same region but created at different times, the multiple SAR images created at different times are archived as time-series data.

[0050] The detected object database 28 stores the details of detected objects identified from changes in the state of SAR images within the detection target area TA. The details of detected objects include the type, shape information, and time information of the detected object. The type of detected object includes, for example, a vehicle, a parked object on the road, and a missing part of the road. The time information indicates the time when the change in shape occurred. The shape information of detected objects can be composed of the following information for each type, for example:

[0051] Information M1: Coordinates (e.g., latitude and longitude) of the center position of an object considered to be a vehicle 3. Information M2: Line data connecting the start point and end point of an event considered to be on-street parking. The data content of the line data may be, for example, coordinates (e.g., latitude and longitude) of the start point position and end point position, or the extension direction and distance to the start point position and end point position. Information M3: Polygon data representing the range considered to be missing from the road.

[0052] A member database (not shown) is also created in the storage unit 22. The member database includes personal information such as the addresses and names of registered members. Registered members include, for example, owners of vehicles 3, owners of user terminals 6, and operation managers of other servers. The member database also includes identification information (e.g., at least one of MAC addresses, email addresses, and telephone numbers) of the communication devices (e.g., the in-vehicle device 4 and the user terminal 6) of registered members.

[0053] 2, the in-vehicle device 4 is an in-vehicle network including, for example, a processing unit 31 including an ECU (Electric Control Unit), a storage unit 32, and a communication unit 33. The processing unit 31 includes at least one ECU, which is made up of an arithmetic processing device including a CPU that reads a computer program 34 stored in a non-volatile memory of the storage unit 32 and performs various information processing in accordance with the program 34.

[0054] The storage unit 32 is a storage device that includes at least one non-volatile memory (storage medium) of an HDD and an SSD, and a volatile memory (storage medium) made of a random access memory, etc. The computer program 34 of the in-vehicle device 4 includes a driving control program to be executed by the ECU of the processing unit 31, a route search program for the probe vehicle 3, and an image processing program for displaying the searched route on the display of the navigation device.

[0055] The communication unit 33 is a wireless communication device permanently mounted on the probe vehicle 3, or a data communication terminal (for example, a smartphone, a tablet computer, or a node-type personal computer) temporarily mounted on the probe vehicle 3. The communication unit 33 has a GNSS (Global Navigation Satellite System) receiver. The processing unit 31 monitors the current position of the vehicle in almost real time based on the GNSS position information received by the communication unit 33.

[0056] The processing unit 31 records various vehicle attribute data, such as vehicle speed, vehicle direction, steering angle, engine RPM, brake pressure, and accelerator pedal position, measured at a predetermined sensing period, together with the vehicle position and the measurement time, in the storage unit 32. When the vehicle attribute data for a predetermined time (e.g., 10 seconds) has been accumulated in the storage unit 32, the communication unit 33 generates probe information including the accumulated vehicle attribute data and identification information of the vehicle itself, and transmits the generated probe information via uplink to the information providing server 2.

[0057] The on-board device 4 of the probe vehicle 3 includes an input interface that accepts operational inputs from the driver and a display that presents map images corresponding to the digital road map data (DRM) to the driver. The input interface is, for example, an input device attached to the navigation device, or an input device of a data communication terminal mounted on the probe vehicle 3. The display is, for example, a liquid crystal display attached to the navigation device, or a head-up display that projects an image onto the glass surface in front of the driver's seat.

[0058] [Example of Generation and Distribution Process of Information to be Provided] Fig. 3 is a flowchart showing an example of generation and distribution process of information to be provided executed by the control unit 21 of the information providing server 2. The generation and distribution process of information to be provided in Fig. 3 includes the following processes. Note that, of the following processes, it is possible to perform only one of the distribution process of the latest change (step ST14) and the distribution process of the prediction information (step ST15).

[0059] Step ST11: Extraction process of vehicle existence range Step ST12: Generation process of detection target range Step ST13: Object detection process Step ST14: Distribution process of latest change Step ST15: Distribution process of prediction information

[0060] (Vehicle Presence Area Extraction Process) The vehicle presence area extraction process is a process of extracting an area where the vehicle 3 may be present from the digital road map data (DRM) of a predetermined area read from the map database 24 (step ST11).

[0061] The extraction process in step ST11 is a process of generating polygon data representing the outline of a road based on, for example, the position information of a specific link in the DRM, the number of lanes, and the road width of each lane. Note that if the range of a parking lot is included in the background data of the DRM, polygon data of the parking lot may be generated.

[0062] (Detection target range generation process) The detection target range TA generation process is a process of generating the detection target range TA (step ST12) based on the vehicle existence range extracted in step ST11 and the optical image data of the predetermined area read from the optical image database 25. The generated detection target range TA is stored in the target range database 26.

[0063] The generation process of step ST12 includes, for example, the following processes: Process 1: Read optical image data of a specified area. Process 2: Identify excluded objects from the optical image data using a trained learning device that can recognize excluded objects such as medians, shrubs, traffic lights, signs, utility poles, shadows of buildings, and pedestrian bridges. Process 3: Remove the excluded objects from the optical image data, and generate polygon data representing the outline of roads and polygon data representing the outline of parking lots based on the optical image data.

[0064] Process 4: If there is a discrepancy in shape or position between the polygon data based on the DRM and the polygon data based on the optical image data, correct the shape and position to match the latter. Process 5: The corrected polygon data is used as polygon data PD1 representing the outline of the road and polygon data PD2 representing the outline of the parking lot.

[0065] (Object Detection Processing) The object detection processing is processing for detecting a change in state (change in scattering intensity) of the SAR image SIi at a predetermined time point within the detection target range TA (step ST13). The detection of the change in state in step ST13 is performed based on whether or not a predetermined change has occurred in the SAR image SIi at a predetermined time point within the detection target range TA compared to the SAR image prior to that time point.

[0066] When determining the change in the state of the SAR image at each predetermined period, the detection of the change in state in step ST13 is performed based on whether the difference between the two SAR images SIm, SIn (m<n) selected for comparison in the current period corresponds to a predetermined type of change in state.

[0067] Of the two SAR images SIm and SIn, the newer SAR image SIn may be, for example, the SAR image SIk at the generation time point k (hereinafter referred to as the "most recent time point k") that has the smallest time difference from the determination time point of the status change in the current cycle. This is because it is preferable to notify the user of the most recent status change. However, depending on the progression cycle of the generation time points (e.g., every day, every hour, every 10 minutes, etc.) and the type of event to be detected, for example, the SAR image SIk-1 at the generation time point k-1 that is one time before the most recent time point k may be used as the newer SAR image SIn.

[0068] Of the two SAR images SIm, SIn, the older SAR image SIm may be, for example, the SAR image SIk-1 that is one image before the most recent time point k. However, depending on the progression cycle of the generation time point (for example, every day, every hour, every 10 minutes, etc.) and the type of event to be detected, for example, the older SAR image SIm may be, for example, the SAR image SIm that is two or more images before the most recent time point k (m≦k-2).

[0069] For example, if the detected state change is "the appearance of a vehicle 3," the coordinates of the center position of the vehicle 3 and the time information of the state change are recorded in the detected object database 28. If the detected state change is "parked on the street," the line data connecting the start point and end point of the on-street parking and the time information of the state change are recorded in the detected object database 28. Furthermore, if the detected state change is "missing part of the road," the polygon data representing the range of the missing part and the time information of the state change are recorded in the detected object database 28.

[0070] (Latest Change Distribution Process) The latest change distribution process is a process of distributing the latest detected object recorded in the detected object database 28 (step ST14). Specifically, each time a new detected object is stored in the database 28 by the object detection process, the type, shape information, and time information of the stored detected object are transmitted to the in-vehicle device 4 and the user terminal 6.

[0071] (Prediction Information Distribution Process) The prediction information distribution process is a process of creating statistical information on detected objects by time period for each predetermined road or parking lot, and distributing the created statistical information as prediction information (step ST15). Specifically, for example, when a predetermined type of detected object, such as on-street parking, is detected with a high frequency during a predetermined time period, prediction information including shape information indicating the section in which the on-street parking occurs and the time period during which the on-street parking is likely to occur is transmitted to the in-vehicle device 4 and the user terminal 6 as a possible future event.

[0072] At least one of the recommended destinations and routes may be provided to the in-vehicle device 4, the user terminal 6, etc. In this case, by generating the latest status change and prediction information of the SAR image SIi, it is possible to suggest to the driver of the vehicle 2 parking lots that are predicted to be vacant and a comfortable route to those parking lots.

[0073] [Another Example of Generation and Distribution Processing of Information to be Provided] Fig. 4 is a flowchart showing another example of generation and distribution processing of information to be provided, which is executed by the control unit 21 of the information providing server 2. The generation and distribution processing of information to be provided in Fig. 4 includes the following processes. Note that, of the following processes, it is also possible to perform only one of the distribution processing of the latest change (step ST25) and the distribution processing of the prediction information (step ST26).

[0074] Step ST21: Extraction process of vehicle existence range Step ST22: Generation process of detection target range Step ST23: Generation process of background SAR image Step ST24: Object detection process Step ST25: Distribution process of latest change Step ST26: Distribution process of prediction information

[0075] (Vehicle presence range extraction process) The vehicle presence range extraction process (step ST21) is the same as the extraction process (step ST11) in Fig. 3. (Detection target range generation process) The detection target range extraction process (step ST22) is the same as the generation process (step ST12) in Fig. 3.

[0076] (Background SAR Image Generation Process) The background SAR image generation process is a process of generating a background SAR image of the detection target range TA based on SAR images SIi at multiple points in time archived in the SAR image database 27 (step ST23). The generated background SAR image is stored in the background SAR image database 27A.

[0077] Specifically, the generation process generates a SAR image that can be considered to be a static background in the detection target range TA by excluding objects that can be considered to be moving in the detection target range TA (for example, a vehicle 3 waiting at a traffic light). Whether a detected object is moving or not can be determined, for example, based on whether an object detected in the SAR image disappears after a few minutes or whether it is determined to be a moving vehicle 3 that transmitted probe information. Furthermore, whether a detected object is moving or not may be determined by an algorithm (moving target detection) that divides the SAR image into multiple looks at regular intervals.

[0078] (Object Detection Processing) The object detection processing is processing for detecting a change in state (change in scattering intensity) of the SAR image SIi at a predetermined time point within the detection target range TA (step ST24). The detection of the change in state in step ST24 is performed based on whether or not a predetermined change has occurred in the SAR image SIi at a predetermined time point within the detection target range TA compared to the background SAR image generated before that time point.

[0079] When determining the change in the state of the SAR image at each predetermined period, the detection of the change in state in step ST24 is performed by determining whether the difference between one SAR image SIn selected as the comparison target in the current period and a background SAR image generated from multiple SAR images SI that were generated before the SAR image SIn corresponds to a predetermined type of change in state.

[0080] The SAR image SIn to be compared with the background SAR image may also be, for example, the SAR image SIk at the most recent time point k. However, depending on the cycle of the generation time point (e.g., every day, every hour, every 10 minutes, etc.) and the type of event to be detected, for example, the SAR image SIk-1 at the generation time point k-1 immediately preceding the most recent time point k may also be used as the SAR image SIn to be compared with the background SAR image.

[0081] For example, if the detected state change is "the appearance of a vehicle 3," the coordinates of the center position of the vehicle 3 and the time information of the state change are recorded in the detected object database 28. If the detected state change is "parked on the street," the line data connecting the start point and end point of the on-street parking and the time information of the state change are recorded in the detected object database 28. Furthermore, if the detected state change is "missing part of the road," the polygon data representing the range of the missing part and the time information of the state change are recorded in the detected object database 28.

[0082] In the object detection process (step ST24) of FIG. 4, the background SAR image is used to determine the state change of the latest SAR image SIi, which has the advantage of reducing false detections and improving detection accuracy compared to the object detection process (step ST13) of FIG. 3, which uses the past SAR image SIi-1 as is.

[0083] (Latest Change Distribution Process) The latest change distribution process (step ST25) is the same as the distribution process (step ST14) in Fig. 3. (Prediction Information Distribution Process) The prediction information distribution process (step ST26) is the same as the distribution process (step ST15) in Fig. 3.

[0084] [Example of Road Map Screen Display] Fig. 5 is an explanatory diagram showing an example of a road map screen display including events provided by the information providing server 2. The road map in Fig. 5 is a road map displayed on the display of the in-vehicle device 4 or the user terminal 6 that has received the provided information from the information providing server 2. As shown in Fig. 5, events related to road traffic provided by the server 2 include, for example, the following events:

[0085] Event E1: Parking on the shoulder Event E2: Obstacle present Event E3: Possibility of road collapse Event E4: Traffic jam at the exit of the expressway Event E5: Full parking lot Event E6: Empty parking lot Event E7: Traffic jam section

[0086] Of the above events, the event E7 indicated by diagonal hatching is an event generated based on probe information, while the other events E1 to E6 are events generated based on SAR images. Here, since the probe information is data representing the travel trajectory of the traveling probe vehicle 3, it is difficult to determine from the probe information the occurrence of roadside parking (event E1), where the engine is stopped and the vehicle is parked, obstacles (event E2), the state of the parking lot (events E5 and E6), and so on.

[0087] In contrast, the information providing server 2 of this embodiment can detect changes in the state of the SAR image that occur within the detection range TA (such as the appearance of a vehicle 3, on-street parking, and missing roads). Therefore, for example, events E1 to E6, which cannot be generated when the original data is probe information, can be generated from the SAR image, as shown in FIG. 5, and the generated events E1 to E6 can be provided to the user.

[0088] 5, the following events may be detected based on changes in the state of the SAR image, and the detected events may be used as provided information: Event E8: A signboard (e.g., a construction sign) Event E9: An impassable section such as an icy section, a snow-covered section, or a flooded section Event E9 can be detected, for example, by determining whether the reflection intensity of the entire road has significantly decreased.

[0089] Event E10: stranded vehicle. Event E10 can be detected, for example, by determining whether a vehicle 3 is present on the road for a long period of time during bad weather such as snow or heavy rain. Event E11: recommended parking lot. Event E11 can be generated, for example, by determining the vacant parking lot with the fewest number of parked vehicles. Event E12: congestion at facility entrance / exit. Event E12 can be detected, for example, by determining whether the upstream position of the congestion is located near the entrance / exit of a specified facility such as a suburban store.

[0090] Event E13: A puddle on the road. Event E13 can be detected, for example, by determining whether or not an area with reduced reflection intensity exists over a predetermined range or more in a predetermined road section. Event E14: A flooded section of the road. Event E14 can be detected, for example, by determining whether or not a section with reduced reflection intensity exists over a predetermined length or more in a predetermined road section.

[0091] [Other Modifications] The embodiments disclosed herein are illustrative in all respects and are not restrictive. The scope of the present invention is not limited to the above-described embodiments, but includes all modifications within the scope of the claims and equivalents thereof.

[0092] In the above-described embodiment, the detection target area TA may be created only from the digital road map (DRM) or only from the optical image. The optical image server 11 may also generate the detection target area TA. In this case, the information providing server 2 simply records the detection target area TA acquired from the optical image server 11 in its own database 26.

[0093] REFERENCE SIGNS LIST 1 Information provision system 2 Information provision server 3 Vehicle 4 In-vehicle device 5 User 6 User terminal 7 Wireless base station 8 Public communication network 10 SAR image server 11 Optical image server 21 Control unit 22 Memory unit (database storage) 23 Communication unit 24 Map database 25 Optical image database 26 Target range database 27 SAR image database 27A Background SAR image database 28 Detected object database 29 Computer program 31 Processing unit 32 Memory unit 33 Communication unit 34 Computer program DRM Digital map data TA Detection target range PD1 Polygon data (road) PD2 Polygon data (parking lot) SIi SAR image

Claims

1. An information processing device that generates events related to road traffic, comprising: a storage device of the information processing device itself or an external device that stores a detection range in which a vehicle may be present and SAR images at multiple points in time that include the detection range; and a control unit that detects changes in the state of the SAR images within the detection range and generates the event based on the detected changes in state.

2. The information processing device according to claim 1, wherein the state change detected by the control unit is a state change of the SAR image at the time of generation relative to the SAR image at a time prior to a predetermined generation time.

3. The information processing device of claim 1, wherein the storage further stores a background SAR image obtained by excluding moving objects from the plurality of SAR images, and the state change detected by the control unit is a state change of the SAR image at the time of generation relative to the background SAR image prior to a specified generation time.

4. An information processing device according to any one of claims 1 to 3, wherein the control unit further generates prediction information based on statistical information of the detected state changes.

5. An information processing device as claimed in any one of claims 1 to 4, wherein the events include at least one of: shoulder parking, presence of an obstacle, possibility of a road collapse, congestion at a highway exit, a parking lot being full, and a parking lot being vacant.

6. An information processing device according to any one of claims 1 to 5, wherein the events include at least one of: a signboard, a difficult-to-pass section, a stranded vehicle, a recommended parking lot, congestion at an entrance / exit to a facility, a puddle on the road, and a flooded section of the road.

7. A computer program that causes a computer to function as an information processing device that generates events related to road traffic, the computer functioning as: a storage of the computer's own device or an external device that stores a detection range in which a vehicle may be present and SAR images at multiple points in time that include the detection range; and a control unit that detects changes in the state of the SAR images within the detection range and generates the event based on the detected changes in state.

8. An information generation method executed by a device that generates events related to road traffic, comprising: a step in which a storage of the device or an external device stores a detection range in which a vehicle may be present and SAR images at multiple points in time that include the detection range; and a step in which a control unit of the device detects changes in the state of the SAR image within the detection range and generates the event based on the detected changes in state.

Images